• Medientyp: E-Book
  • Titel: Machine learning : a probabilistic perspective
  • Beteiligte: Murphy, Kevin P. [VerfasserIn]
  • Erschienen: Cambridge, Massachusetts; London, England: The MIT Press, [2012]
  • Erschienen in: ProQuest Ebook Central
    Adaptive Computation and Machine Learning Series
  • Umfang: 1 Online-Ressource (XXIX, 1071 Seiten); Diagramme
  • Sprache: Englisch
  • ISBN: 9780262305242
  • Entstehung:
  • RVK-Notation: ST 300 : Allgemeines
    ST 278 : Mensch-Maschine-Kommunikation Software-Ergonomie
  • Schlagwörter: Maschinelles Lernen > Wahrscheinlichkeitstheorie
  • Beschreibung: A comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach.

    Intro -- Contents -- Preface -- 1 Introduction -- 2 Probability -- 3 Generative Models for Discrete Data -- 4 Gaussian Models -- 5 Bayesian Statistics -- 6 Frequentist Statistics -- 7 Linear Regression -- 8 Logistic Regression -- 9 Generalized Linear Models and the Exponential Family -- 10 Directed Graphical Models (Bayes Nets) -- 11 Mixture Models and the EM Algorithm -- 12 Latent Linear Models -- 13 Sparse Linear Models -- 14 Kernels -- 15 Gaussian Processes -- 16 Adaptive Basis Function Models -- 17 Markov and Hidden Markov Models -- 18 State Space Models -- 19 Undirected Graphical Models (Markov Random Fields) -- 20 Exact Inference for Graphical Models -- 21 Variational Inference -- 22 More Variational Inference -- 23 Monte Carlo Inference -- 24 Markov Chain Monte Carlo (MCMC) Inference -- 25 Clustering -- 26 Graphical Model Structure Learning -- 27 Latent Variable Models for Discrete Data -- 28 Deep Learning -- Notation -- Bibliography -- Index to Code -- Index to Keywords.
  • Anmerkungen: Includes bibliographical references and index. - Electronic reproduction; Palo Alto, Calif; ebrary; 2011; Available via World Wide Web; Access may be limited to ebrary affiliated libraries